Machine learning (ML) methods proved to be a useful tool in chemistry [1]}, [2]}, [3]}, [4]} and materials science [5]}, [6]}, [7]}. Their use is mainly focused on determining the relation between the structure of a chemical compound and a certain property [8]}, [9]}. Further, found relations may be applied for searching new compounds with the desired value of the property. Recently, the artificial neural networks (ANN) has become one of the most popular (ML) approaches due to the high accuracy and the flexibility of the architectures [10]}, [11]}. The ANN structure is a set of layers, each of them consisting of one or more nodes, and each node, in turn, can be described by numerical values - weights and biases. These values are fitted during the training process to minimize the deviation between the target value and its approximation). The layered structure allows to increase the level of abstraction layer by layer going from the description of the object to its properties. One of the main limitations for the application of ANN in the modern cheminformatics is a demand for large sets of data (hundreds to millions data points) for model training [9]}, [13]}, [14]}. At the same time, most of the chemical and biological properties, do not have enough experimental data collected; the size of datasets for such properties often does not exceed tens or hundreds of compounds. This list includes substances related to the biological target activities, or the substances for which synthesis and measurement is difficult or impossible for some reasons [15]}, [16]}. One of the most promising methods of working with such datasets is the transfer learning approach. Recently, this methodology has found major interest in the scientific community, including chemistry and materials science fields [17]}, [18]}, [19]}. Its main idea is to transfer part of the information obtained from a large dataset to a problem with the lack of training data. Generally, it is represented in the transfer of some parameters such as mentioned above weights and biases from a pre-trained model (‘donor’ model) to a model that is supposed to be trained on a small dataset (‘acceptor’ model). As it is known from the image recognition field, the procedure works well for similar datasets [20]}. In case of chemistry, it is supposed to transfer general information about chemical structures to the acceptor model, leaving algorithm to fit it for the specific property. It should be noted that in addition to the property similarity of the donor and acceptor dataset, it is necessary that the donor dataset should be as large and diverse as possible. This helps to generalize transferable parameters of the model and thus increases the applicability domain (AD) of the donor model, which will consequently improve the quality and the AD of the acceptor model.
